{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#|default_exp models.XResNet1d"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# XResNet1d"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">This is a modified version of fastai's XResNet model in github"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#|export\n",
    "from fastai.vision.models.xresnet import *\n",
    "from tsai.imports import *\n",
    "from tsai.models.layers import *\n",
    "from tsai.models.utils import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#|export\n",
    "@delegates(ResBlock)\n",
    "def xresnet1d18 (c_in, c_out, act=nn.ReLU, **kwargs): return xresnet18(c_in=c_in, n_out=c_out, act_cls=act, ndim=1, **kwargs)\n",
    "@delegates(ResBlock)\n",
    "def xresnet1d34 (c_in, c_out, act=nn.ReLU, **kwargs): return xresnet34(c_in=c_in, n_out=c_out, act_cls=act, ndim=1, **kwargs)\n",
    "@delegates(ResBlock)\n",
    "def xresnet1d50 (c_in, c_out, act=nn.ReLU, **kwargs): return xresnet50(c_in=c_in, n_out=c_out, act_cls=act, ndim=1, **kwargs)\n",
    "@delegates(ResBlock)\n",
    "def xresnet1d101 (c_in, c_out, act=nn.ReLU, **kwargs): return xresnet101(c_in=c_in, n_out=c_out, act_cls=act, ndim=1, **kwargs)\n",
    "@delegates(ResBlock)\n",
    "def xresnet1d152 (c_in, c_out, act=nn.ReLU, **kwargs): return xresnet152(c_in=c_in, n_out=c_out, act_cls=act, ndim=1, **kwargs)\n",
    "@delegates(ResBlock)\n",
    "def xresnet1d18_deep (c_in, c_out, act=nn.ReLU, **kwargs): return xresnet18_deep(c_in=c_in, n_out=c_out, act_cls=act, ndim=1, **kwargs)\n",
    "@delegates(ResBlock)\n",
    "def xresnet1d34_deep (c_in, c_out, act=nn.ReLU, **kwargs): return xresnet34_deep(c_in=c_in, n_out=c_out, act_cls=act, ndim=1, **kwargs)\n",
    "@delegates(ResBlock)\n",
    "def xresnet1d50_deep (c_in, c_out, act=nn.ReLU, **kwargs): return xresnet50_deep(c_in=c_in, n_out=c_out, act_cls=act, ndim=1, **kwargs)\n",
    "@delegates(ResBlock)\n",
    "def xresnet1d18_deeper (c_in, c_out, act=nn.ReLU, **kwargs): return xresnet18_deeper(c_in=c_in, n_out=c_out, act_cls=act, ndim=1, **kwargs)\n",
    "@delegates(ResBlock)\n",
    "def xresnet1d34_deeper (c_in, c_out, act=nn.ReLU, **kwargs): return xresnet34_deeper(c_in=c_in, n_out=c_out, act_cls=act, ndim=1, **kwargs)\n",
    "@delegates(ResBlock)\n",
    "def xresnet1d50_deeper (c_in, c_out, act=nn.ReLU, **kwargs): return xresnet50_deeper(c_in=c_in, n_out=c_out, act_cls=act, ndim=1, **kwargs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 xresnet1d18\n",
      "1 xresnet1d34\n",
      "2 xresnet1d50\n",
      "3 xresnet1d18_deep\n",
      "4 xresnet1d34_deep\n",
      "5 xresnet1d50_deep\n",
      "6 xresnet1d18_deeper\n",
      "7 xresnet1d34_deeper\n",
      "8 xresnet1d50_deeper\n"
     ]
    }
   ],
   "source": [
    "bs, c_in, seq_len = 2, 4, 32\n",
    "c_out = 2\n",
    "x = torch.rand(bs, c_in, seq_len)\n",
    "archs = [\n",
    "    xresnet1d18, xresnet1d34, xresnet1d50, \n",
    "    xresnet1d18_deep, xresnet1d34_deep, xresnet1d50_deep, xresnet1d18_deeper,\n",
    "    xresnet1d34_deeper, xresnet1d50_deeper\n",
    "#     # Long test\n",
    "#     xresnet1d101, xresnet1d152,\n",
    "]\n",
    "for i, arch in enumerate(archs):\n",
    "    print(i, arch.__name__)\n",
    "    test_eq(arch(c_in, c_out, sa=True, act=Mish)(x).shape, (bs, c_out))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "m = xresnet1d34(4, 2, act=Mish)\n",
    "test_eq(len(get_layers(m, is_bn)), 38)\n",
    "test_eq(check_weight(m, is_bn)[0].sum(), 22)"
   ]
  },
  {
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   "execution_count": null,
   "metadata": {},
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     "text": [
      "/Users/nacho/notebooks/tsai/nbs/112_models.XResNet1d.ipynb saved at 2022-11-09 13:08:29\n",
      "Correct notebook to script conversion! 😃\n",
      "Wednesday 09/11/22 13:08:32 CET\n"
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   ],
   "source": [
    "#|eval: false\n",
    "#|hide\n",
    "from tsai.export import get_nb_name; nb_name = get_nb_name(locals())\n",
    "from tsai.imports import create_scripts; create_scripts(nb_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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